Polak, Adi
Scaling machine learning with Spark : distributed ML with MLlib, TensorFlow, and PyTorch / Adi Polak. -- First edition. -- Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo : O'Reilly, 2023. -- xix, 270 stran : ilustrace ; 24 cm. -- Obsahuje rejstřík. -- Resumé: "Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better. Scaling machine learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology. You will: Explore machine learning, including distributed computing concepts and terminology ; Manage the ML lifecycle with MLflow ; Ingest data and perform basic preprocessing with Spark ; Explore feature engineering, and use Spark to extract features ; Train a model with MLlib and build a pipeline to reproduce it ; Build a data system to combine the power of Spark with deep learning ; Get a step-by-step example of working with distributed TensorFlow ; Use PyTorch to scale machine learning and its internal architecture."--Nakladatelská anotace. -- ISBN : 978-1-0981-0682-9 (brožováno).
otevřený software. Apache (software). frameworky. big data. zpracování dat. učící se systémy. strojové učení. příručky
004.42. 004.6-022.257. 004.62. 004.85. 004.42Apache. 004.4.057.8. (035)